Package de.lmu.ifi.dbs.elki.database

Examples of de.lmu.ifi.dbs.elki.database.Database

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    this.projectors = projectors;
    this.factories = factories;

    // Ensure that various common results needed by visualizers are
    // automatically created
    final Database db = ResultUtil.findDatabase(result);
    ResultUtil.ensureClusteringResult(db, result);
    this.selection = ResultUtil.ensureSelectionResult(db);
    for(Relation<?> rel : ResultUtil.getRelations(result)) {
      ResultUtil.getSamplingResult(rel);
      // FIXME: this is a really ugly workaround. :-(
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   * Generate a default (fallback) clustering.
   *
   * @return generated clustering
   */
  private Clustering<Model> generateDefaultClustering() {
    final Database db = ResultUtil.findDatabase(getResult());
    Clustering<Model> c = null;
    try {
      // Try to cluster by labels
      ByLabelHierarchicalClustering split = new ByLabelHierarchicalClustering();
      c = split.run(db);
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   *         occurs
   * @throws ParameterException if the parameter setting is wrong
   */
  private Matrix runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) throws UnableToComplyException, ParameterException {
    // build database for derivator
    Database derivatorDB = buildDerivatorDB(relation, interval);

    // set the parameters
    ListParameterization parameters = new ListParameterization();
    parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
    parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dim - 1));
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   * @return a basis of the found subspace
   */
  private LinearEquationSystem runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) {
    try {
      // build database for derivator
      Database derivatorDB = buildDerivatorDB(relation, ids);

      ListParameterization parameters = new ListParameterization();
      parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
      parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dimensionality));
      DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
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    ListParameterization params = new ListParameterization();
    // Input
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, params);
    db.initialize();

    // verify data set size.
    Relation<?> rel = db.getRelation(TypeUtil.ANY);
    assertTrue(rel.size() == shoulds);

    // run all-in-one
    TrivialAllInOne allinone = new TrivialAllInOne();
    Clustering<Model> rai = allinone.run(db);
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        filterlist.add(filter);
      }
    }
    params.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filterlist);
    params.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, params);

    testParameterizationOk(params);

    db.initialize();
    Relation<?> rel = db.getRelation(TypeUtil.ANY);
    org.junit.Assert.assertEquals("Database size does not match.", expectedSize, rel.size());
    return db;
  }
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    List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class });
    inputparams.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filters);
    inputparams.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
    inputparams.failOnErrors();

    db.initialize();
    Relation<NumberVector<?, ?>> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // verify data set size.
    org.junit.Assert.assertEquals("Database size does not match.", shoulds, relation.size());

    // Euclidean
    {
      DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
      KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);

      MeanVariance meansize = new MeanVariance();
      for(DBID id : relation.iterDBIDs()) {
        KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
        meansize.put(knnlist.size());
      }
      org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
      org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
    }
    // Manhattan
    {
      DistanceQuery<NumberVector<?, ?>, DoubleDistance> dq = db.getDistanceQuery(relation, ManhattanDistanceFunction.STATIC);
      KNNQuery<NumberVector<?, ?>, DoubleDistance> knnq = QueryUtil.getLinearScanKNNQuery(dq);

      MeanVariance meansize = new MeanVariance();
      for(DBID id : relation.iterDBIDs()) {
        KNNResult<DoubleDistance> knnlist = knnq.getKNNForDBID(id, 2);
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    List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class });
    inputparams.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filters);
    inputparams.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
    inputparams.failOnErrors();

    db.initialize();
    Relation<NumberVector<?, ?>> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // verify data set size.
    org.junit.Assert.assertEquals("Database size does not match.", shoulds, relation.size());

    // Euclidean
    {
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* @author Lucia Cichella
*/
public class TestGaussianUniformMixture extends AbstractSimpleAlgorithmTest implements JUnit4Test {
  @Test
  public void testGaussianUniformMixture() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);

    // Parameterization
    ListParameterization params = new ListParameterization();

    // setup Algorithm
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* @author Lucia Cichella
*/
public class TestOPTICSOF extends AbstractSimpleAlgorithmTest implements JUnit4Test {
  @Test
  public void testOPTICSOF() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-parabolic.ascii", 530);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(OPTICS.MINPTS_ID, 22);

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Related Classes of de.lmu.ifi.dbs.elki.database.Database

  • de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestCASHResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestCOPACResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestERiCResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestFourCResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestORCLUSResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.TestKMeansResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.TestCLIQUEResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.TestDiSHResults
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